US-German Collaboration: Unravel CNS regeneration - From Fact Extraction to Experiment Design

美德合作：揭示中枢神经系统再生——从事实提取到实验设计

• 批准号: 1207592
• 负责人: Lawrence Hunter
• 金额: $ 48.79万
• 依托单位: University of Colorado at Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-12-01 至 2017-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1207592&HistoricalAwards=false
• 关键词: US; German; Collaboration; Unravel; CNS

This research is motivated by the problem of spinal cord injuries, with the goal of producing data that can lead to pharmacological or other interventions to make recovery possible. It is hypothesized that major changes after a spinal cord injury occur on the protein level, and that a model of protein changes and interactions between proteins will make it possible to elucidate previously unnoticed relationships between the participating proteins and protein pathways. To do this, a computational neuroscience approach is a necessary addition to laboratory experiments. This analysis requires a considerable amount of background knowledge; to acquire this knowledge, natural language processing (text mining) is necessary; specifically, new techniques for natural language processing need to be developed in the neuroscience domain.The research plan involves a series of proteomics experiments to be carried out on rats with induced spinal cord injuries and the interpretation of these results using a computational systems biology approach. The quality of knowledge-based computational analysis depends critically on the breadth of formally represented knowledge in the program. A major challenge is that much of the requisite knowledge is "buried" in scientific publications, rather than being available in computable form in databases. Therefore, novel text mining techniques will be developed, tailored for the neuroscience domain, to extract such knowledge from scientific publications and convert it into a computable form. A generic computational analytical tool, known as Hanalyzer, is already available but needs to be adapted to the specific problem; the challenge is to develop the natural language processing technology. A neuroscience-specific aspect of this challenge is that there is a high diversity in the surface forms of words that are used to refer to spinal cord regeneration, making machine-learning-based approaches susceptible to data sparsity and rule-based approaches vulnerable to an intractable number of keywords that must be accounted for. A distributional approach will be used to approach this challenge, with novel techniques that make use of semantic role labeling, recognition of ontological concepts, and dependency parsing to learn the surface forms that correspond to abstract or implicit concepts like spinal cord regeneration.This project is a collaboration involving investigators in Denver, Colorado and in Duesseldorf, Germany. A companion project is being funded by the German Ministry of Education and Research (BMBF).

这项研究的动机是脊髓损伤问题，目的是产生数据，可以导致药理学或其他干预措施，使恢复成为可能。据推测，脊髓损伤后的主要变化发生在蛋白质水平上，蛋白质变化和蛋白质之间相互作用的模型将有可能阐明以前未被注意到的参与蛋白质和蛋白质途径之间的关系。要做到这一点，计算神经科学方法是实验室实验的必要补充。这种分析需要相当多的背景知识；为了获得这些知识，自然语言处理（文本挖掘）是必要的；具体来说，自然语言处理的新技术需要在神经科学领域得到发展。该研究计划包括在脊髓损伤大鼠身上进行一系列蛋白质组学实验，并使用计算系统生物学方法对这些结果进行解释。基于知识的计算分析的质量主要取决于程序中正式表示的知识的广度。一个主要的挑战是，许多必要的知识“埋藏”在科学出版物中，而不是在数据库中以可计算的形式提供。因此，新的文本挖掘技术将被开发出来，为神经科学领域量身定制，从科学出版物中提取这些知识并将其转换为可计算的形式。一种被称为Hanalyzer的通用计算分析工具已经可用，但需要针对具体问题进行调整；自然语言处理技术的发展是一个挑战。这一挑战的一个神经科学方面是，用于指代脊髓再生的单词的表面形式存在高度多样性，这使得基于机器学习的方法容易受到数据稀疏性的影响，而基于规则的方法容易受到必须考虑的棘手关键字数量的影响。分布式方法将被用来应对这一挑战，使用新的技术，利用语义角色标记、本体概念识别和依赖关系解析来学习与抽象或隐含概念（如脊髓再生）相对应的表面形式。这个项目是由科罗拉多州丹佛市和德国杜塞尔多夫的研究人员合作完成的。德国教育和研究部（BMBF）正在资助一个伙伴项目。